Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting
View a PDF of the paper titled Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting, authored by Hongqiang Lin and six other contributors, provides critical insights into reducing overfitting in skill optimization for large language model (LLM) agents.
Abstract:
Enabling large language model (LLM) agents to accumulate and reuse experience from past interactions remains a central challenge in real-world applications. A promising solution is to treat skills as trainable states and optimize them in the same way as model parameters in neural network training. However, data-driven skill optimization is prone to overfitting to the limited trajectories collected from real environments. Overexploiting these trajectories overfits the current batch, while unconstrained exploration causes regression on previously solved cases. This tension motivates a constrained search view of skill self-evolution, governed by an exploration–exploitation trade-off. We propose SkillBoost, a three-stage framework that mitigates both risks: structured exploitation localizes observed failures to editable skill components, prior-guided exploration draws on prior knowledge in the LLM to generate diverse repair candidates, and verified acceptance commits a candidate only when it improves performance within a regression bound. Experiments across 23 model–benchmark configurations show that SkillBoost achieves state-of-the-art performance while mitigating overfitting, outperforming both human-crafted and LLM-generated skills. Transfer experiments further show that optimized skills can be reused by other agents on similar tasks.
Submission History
From: Hongqiang Lin [view email]
- [v1] Wed, 29 Jul 2026 09:05:40 UTC (663 KB)
- [v2] Wed, 19 Aug 2026 08:06:05 UTC (663 KB)
Understanding the Challenge of Skill Overfitting
Skill overfitting in large language models presents a considerable hurdle in their deployment for practical applications. While LLMs are designed to learn from vast amounts of data, they often face challenges when absorbing information from limited interaction trajectories. These limited data sets can lead models to become too tailored to specific experiences, resulting in degraded performance in broader scenarios. The research addresses this pressing issue by exploring how LLM agents can innovate while avoiding the pitfalls of overfitting.
Introducing SkillBoost
The authors present a novel solution called SkillBoost, which is structured around a three-stage framework to optimize skill acquisition. The first stage is structured exploitation, focusing on identifying failure points within existing skills. This allows for a targeted approach, addressing specific deficiencies rather than indiscriminately adjusting multiple parameters, which can exacerbate overfitting.
Subsequently, the framework employs prior-guided exploration. By leveraging existing knowledge within the LLM, the model can generate a diverse array of potential solutions. This process encourages creativity and adaptability, essential traits for improving performance across various tasks.
The final stage is verified acceptance, a critical step ensuring that only those skill adjustments which demonstrably enhance performance are adopted. By implementing a regression-bound acceptance criterion, the likelihood of reverting to earlier, less effective strategies is minimized, thereby stabilizing learning outcomes.
Experiments and Performance Outcomes
The efficacy of SkillBoost was validated through extensive experiments encompassing 23 model-benchmark configurations. The results were striking, demonstrating that the SkillBoost framework not only achieves state-of-the-art performance in skill optimization but also effectively mitigates the risk of overfitting. This offers a dual benefit, enhancing the utility and adaptability of LLMs in unpredictable real-world settings.
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Moreover, SkillBoost’s ability to outperform both human-crafted and LLM-generated skills highlights its potential for scalability and versatility. Importantly, the research demonstrated that optimized skills are transferable, allowing agents to apply learned skills to similar tasks effectively.
Implications for Future Research
The findings of this study are poised to have a profound impact on the future of LLMs and artificial intelligence research. The exploration-exploitation trade-off not only sheds light on current methodologies but also poses intriguing questions about the future directions of adaptive learning frameworks. As researchers continue to refine and enhance optimization techniques, the principles laid out in this work could serve as foundational concepts guiding further advancements in the field.
Through ongoing exploration of these methodologies, the potential for developing robust, scalable AI agents is becoming more attainable. As AI systems become increasingly integrated into various sectors, understanding how to leverage historical interaction data for skill improvement will be crucial in elevating their effectiveness and reliability.
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